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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2512.18266 |
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| _version_ | 1866917159995703296 |
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| author | Ma, Lizhi Hu, Yi-Xiang Wang, Yuke Zhao, Yifang Ren, Yihui Liao, Jian-Xiang Wu, Feng Li, Xiang-Yang |
| author_facet | Ma, Lizhi Hu, Yi-Xiang Wang, Yuke Zhao, Yifang Ren, Yihui Liao, Jian-Xiang Wu, Feng Li, Xiang-Yang |
| contents | With the rapid advancements in big data technologies, the Databricks platform has become a cornerstone for enterprises and research institutions, offering high computational efficiency and a robust ecosystem. However, managing the escalating operational costs associated with job execution remains a critical challenge. Existing solutions rely on static configurations or reactive adjustments, which fail to adapt to the dynamic nature of workloads. To address this, we introduce LeJOT, an intelligent job cost orchestration framework that leverages machine learning for execution time prediction and a solver-based optimization model for real-time resource allocation. Unlike conventional scheduling techniques, LeJOT proactively predicts workload demands, dynamically allocates computing resources, and minimizes costs while ensuring performance requirements are met. Experimental results on real-world Databricks workloads demonstrate that LeJOT achieves an average 20% reduction in cloud computing costs within a minute-level scheduling timeframe, outperforming traditional static allocation strategies. Our approach provides a scalable and adaptive solution for cost-efficient job scheduling in Data Lakehouse environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18266 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LeJOT: An Intelligent Job Cost Orchestration Solution for Databricks Platform Ma, Lizhi Hu, Yi-Xiang Wang, Yuke Zhao, Yifang Ren, Yihui Liao, Jian-Xiang Wu, Feng Li, Xiang-Yang Machine Learning With the rapid advancements in big data technologies, the Databricks platform has become a cornerstone for enterprises and research institutions, offering high computational efficiency and a robust ecosystem. However, managing the escalating operational costs associated with job execution remains a critical challenge. Existing solutions rely on static configurations or reactive adjustments, which fail to adapt to the dynamic nature of workloads. To address this, we introduce LeJOT, an intelligent job cost orchestration framework that leverages machine learning for execution time prediction and a solver-based optimization model for real-time resource allocation. Unlike conventional scheduling techniques, LeJOT proactively predicts workload demands, dynamically allocates computing resources, and minimizes costs while ensuring performance requirements are met. Experimental results on real-world Databricks workloads demonstrate that LeJOT achieves an average 20% reduction in cloud computing costs within a minute-level scheduling timeframe, outperforming traditional static allocation strategies. Our approach provides a scalable and adaptive solution for cost-efficient job scheduling in Data Lakehouse environments. |
| title | LeJOT: An Intelligent Job Cost Orchestration Solution for Databricks Platform |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.18266 |